class_docstr: Identical to byteformer.ByteFormer, but registered as an audio classification
  model.
docstr: Get a dummy input and label that could be passed to the model.
  
  Args:
      batch_size: The batch size to use for the generated inputs.
  
  Returns:
      A dict with
          {
              "samples": {"audio": tensor of shape [batch_size, sequence_length]},
              "targets": tensor of shape [batch_size],
          }
docstr_tok: ["Get","a","dummy","input","and","label","that","could","be","passed","to","the","model",".","Args",":","batch_size",":","The","batch","size","to","use","for","the","generated","inputs",".","Returns",":","A","dict","with","{","``","samples","''",":","{","``","audio","''",":","tensor","of","shape","[","batch_size",",","sequence_length","]","}",",","``","targets","''",":","tensor","of","shape","[","batch_size","]",",","}"]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """
      Get a dummy input and label that could be passed to the model.
  
      Args:
          batch_size: The batch size to use for the generated inputs.
  
      Returns:
          A dict with
              {
                  "samples": {"audio": tensor of shape [batch_size, sequence_length]},
                  "targets": tensor of shape [batch_size],
              }
      """
      input_and_label = super().dummy_input_and_label(batch_size)
  
      ret = {
          "samples": {"audio": input_and_label["samples"]},
          "targets": input_and_label["targets"],
      }
      return ret
  
file_path: ml-cvnets/cvnets/models/audio_classification/audio_byteformer.py
imports: import argparse
  from typing import Dict, Union
  from torch import Tensor
  from cvnets.models import MODEL_REGISTRY
  from cvnets.models.audio_classification.base_audio_classification import BaseAudioClassification
  from cvnets.models.classification.byteformer import ByteFormer
label_desc: Use image node_id 2 for calling the AudioByteFormer obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: ret